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Brazilian Judiciary Dictionary

Brazilian Judiciary Dictionary MCP for AI. Find Every Court & Agency Mention in Legal Text

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Connect to your AI in seconds.

Brazilian Judiciary Dictionary Engine uses deterministic regex to find every Brazilian court, tribunal, agency, and regulatory body in any document.

It checks against 100+ pre-indexed entities—STJ, ANVISA, BACEN, etc.—with zero AI guesswork or false positives.

What your AI can do

Search legal entities

Scans text and finds known Brazilian legal entities (courts, tribunals) using a strict offline dictionary search.

Identify all Brazilian judicial bodies

It scans text and reports every instance of Superior Courts, Regional Tribunals (TRF), and State Courts (TJ) mentioned.

Locate regulatory agencies

The engine pulls out specific regulatory organizations, including the CNJ, CVM, ANVISA, and multiple other state/federal bodies.

Count entity mentions by category

It gives a summary count of how many times different types of entities—Superior, Regional, Regulatory—appear across the document set.

Included with Plan

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AI Agent

Brazilian Judiciary Dictionary Engine: 1 Tool

Use the single available tool to search text for known Brazilian legal entities using a strict offline dictionary lookup.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Brazilian Judiciary Dictionary Engine on Vinkius

Search Legal Entities

Scans text and finds known Brazilian legal entities (courts, tribunals) using a strict offline dictionary search.

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Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Brazilian Judiciary Dictionary integration is available immediately — no restart needed.

Choose How to Get Started

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Manually tracking every legal body reference is brutal work.

Today, reviewing a single litigation bundle means opening up dozens of PDFs. You're copy-pasting snippets into spreadsheets and manually cross-referencing names like 'MPF,' 'CNJ,' or 'BACEN.' You spend hours just counting mentions and verifying that every acronym you find is actually on the official list.

With this MCP, your agent handles the whole process. You run a single query, and it returns a clean breakdown of every unique entity found, grouped by category (Superior Courts, Regulatory Bodies, etc.). The tedious manual tallying disappears; you get actionable data immediately.

The search_legal_entities tool delivers the full scope.

Instead of searching one court list at a time, this engine simultaneously checks against all 5 Superior Courts, all 6 TRFs, and over twenty state courts. It’s not just about finding names; it's about knowing which *type* of body it is.

It makes the distinction between general text matching and strict legal dictionary lookup. You get a complete picture of jurisdictional presence every single time.

What your AI can actually do with this

Legal documents are a mess of acronyms and proper nouns; standard language models often fail when counting or identifying specific Brazilian courts. This MCP fixes that by running strict dictionary matching against the entire Brazilian legal structure.

It identifies everything from Superior Tribunals (like STF) to state-level agencies, including regulators like CADE and financial bodies such as BACEN. You feed it a large batch of text, and it returns a count and list of every specific entity found, categorized by its type. Because this process uses pure dictionary matching, you get reliable results that don't rely on the AI guessing what an acronym means.

Accessing this power through Vinkius allows your agent to bring structural accuracy to otherwise messy legal data.

Built · Hosted · Managed by Vinkius Brazilian Judiciary Dictionary Engine - Legal Entity Search
Server ID 019e38b6-7a97-70c9-9047-fea91f562418
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

Does search_legal_entities only work for federal courts? +

No, it covers the entire Brazilian judiciary system. It includes Superior Courts (STF, STJ) as well as all state-level tribunals (TJs), making its scope comprehensive.

Can search_legal_entities find regulatory agencies? +

Yes. The engine is specifically programmed to identify major regulatory bodies like CADE, CVM, ANVISA, and BACEN, treating them as distinct legal entities.

What if I need to search for a country other than Brazil? +

This MCP focuses exclusively on the Brazilian judiciary. If you need another jurisdiction, you'll have to use its custom dictionary parameter or an alternative tool set up for that region.

How does search_legal_entities handle acronyms? +

It uses strict regex boundary matching, which is a deterministic process. This means it finds the exact phrase match and won't be confused by context or general AI inference.

Does using `search_legal_entities` require the input text to be in plain format? +

Yes, you must feed it clean, extracted text data. The engine only processes strings and cannot read PDFs, images, or complex file formats directly.

What are the performance considerations when running `search_legal_entities` on massive documents? +

There are no hard-coded rate limits for volume. Processing time depends purely on the document's length and the number of unique entities it contains.

How does `search_legal_entities` handle slight variations, like hyphens or punctuation? +

It requires exact matches based on predefined word boundaries. If an entity name is hyphenated differently or has surrounding punctuation not in the dictionary, it will not detect it.

Is the data processed by `search_legal_entities` secure and private? +

Yes, processing happens locally using pure regex matching. The engine does not transmit or store your source document content anywhere.

What exactly is pre-indexed? +

The complete Brazilian judiciary: 5 Superior Courts, 6 TRFs, 24 TRTs, 27 TJs (including TJDFT), 3 military TJMs, oversight bodies (CNJ, CNMP, TCU), prosecution and advocacy (AGU, MPF, MPT, MPM, MPDFT, DPU, OAB), and 15 regulatory agencies (CADE, CVM, BACEN, INPI, INSS, SUSEP, ANATEL, ANVISA, ANS, ANAC, ANEEL, ANP, ANA, ANTT, ANTAQ).

Does it cover courts from other countries? +

No. This engine covers exclusively the Brazilian legal system. To add courts from other countries, pass a custom JSON dictionary mapping acronyms to full names. Custom entries are tagged separately in the output.

How are results organized? +

Each detected entity includes its acronym, full official name, category (Superior, TRF, TRT, TJ, TJM, Controle, MP/Advocacia, Regulador), and exact mention count. A category summary is also provided for quick analysis.

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